International Journal of Public Administration
ISSN: 0190-0692 (Print) 1532-4265 (Online) Journal homepage: https://www.tandfonline.com/loi/lpad20
An Empirical Investigation of Health Practitioners
Technology Adoption: The Mediating Role of
Electronic Health
Vincent Ekow Arkorful, Zhao Shuliang, Sayibu Muhideen, Ibrahim Basiru &
Anastasia Hammond
To cite this article: Vincent Ekow Arkorful, Zhao Shuliang, Sayibu Muhideen, Ibrahim Basiru
& Anastasia Hammond (2019): An Empirical Investigation of Health Practitioners Technology
Adoption: The Mediating Role of Electronic Health, International Journal of Public Administration,
DOI: 10.1080/01900692.2019.1664569
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Published online: 19 Sep 2019.
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INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION
https://doi.org/10.1080/01900692.2019.1664569
An Empirical Investigation of Health Practitioners Technology Adoption: The
Mediating Role of Electronic Health
Vincent Ekow Arkorfula, Zhao Shulianga, Sayibu Muhideena, Ibrahim Basirua, and Anastasia Hammondb
aSchool of Public Affairs, University of Science and Technology of China, Hefei, Anhui, China; bDepartment of Psychology and Home Science,
University of Ghana, Accra, Legon, Ghana
ABSTRACT KEYWORDS
Health technology innovation integration is rapidly growing in global health-care settings. Technology integration;
However, research on factors driving technology adoption intention is limited. On this note, it universal health coverage;
has become important to investigate and understand the complex factors underpinning practi- mediating role; electronic
tioners’ technology adoption intentions. Drawing on the Technology Acceptance Model and The health; health practitioners’
Institutional Theory, we propose a model to empirically investigate technology adoption and its technology adoption; Ghana
potency in driving universal health coverage, mediated by electronic health technology. We used
data collected from 416 health sector practitioners to empirically test the model. Using the
Structural Equation Modeling technique, the study found that perceived ease of use and relevant
technology infrastructure all have significant positive relationship with universal health coverage.
However, perceived usefulness, management support and adequate practitioner training were
found to have negative relationship with universal health coverage. The results further revealed
that perceived-ease-of-use, perceived usefulness, management support, adequate practitioner
training and relevant technology infrastructure also have positive relationship with electronic
health technology. Moreover, electronic health technology as a mediator was also found to have
significant relationship with universal health coverage. The implications of the findings are
discussed and suggestions for future research are subsequently highlighted.
Introduction
and utilization, more especially in developing countries,
Technology has become indispensable to improving ser- researchers and practitioners alike have over the years
vice delivery and outcomes. The health sector is not an endeavored to identify factors that propel adoption and
exclusion in this regard. For the benefits that technology utilization decisions. However, the relativity of technol-
usage in the health sector (which has been conceptualized ogy usage and adoption across diverse sectors, strata
as electronic health) provides, different systems within the (individual, organizational and inter-organizational)
health sector in different countries, like never before have and populations have rendered such an endeavor chal-
assigned some degree of paramountcy and prominence to lenging (Merrell, 2013; Rogers, 2003; Sugarhood,
electronic health (e-health) technology innovation. Wherton, Procter, Hinder, & Greenhalgh, 2014).
Consequently, this has robustly driven health sector out- Moreover, studies have ascribed social, cultural, finan-
comes monumentally (Chang et al., 2004; Cline & cial, legal and ethical barriers to the challenges coming up
Haynes, 2001; Kreps, 2006; Lorence, Park, & Fox, 2006; at the individual and organizational level making up:
Neuhauser & Kreps, 2003; Rippen & Yasnoff, 2004). users’ lack of awareness of the benefits, low e-health
Technology deployment into the health sector is literacy, interoperability and a deficiency of evidence of
a strategy which obviously has implications for perfor- cost-effectiveness as well as security concerns (Currie &
mance and outcomes. Seddon, 2014). Some aspects of e-health that threatens
Research (Eysenbach & Jadad, 2001; Napoli, 2001) adoption and utilization consist of economic resources
have underscored the pertinence of health technology constraints (Chinnock, Siegfried, & Clarke, 2005), exor-
deployment. In as much as these research findings bitant costs of usage fees, income disparities, excessive
affirm the positive impacts of health technology, they charges for even primary health information systems
equally report on underlying complexities and subtle (Ashraf, 2005), shortage of human resources (Oak,
nuances that characterize their utilization. In the face of 2007), inadequate government policies for a well-defined
the challenges that confront health technology adoption health system that incorporates e-health (Ahern,
CONTACT Zhao Shuliang shulz@ustc.edu.cn School of Public Affairs, University of Science and Technology of China, Hefei, Anhui, China.
© 2019 Taylor & Francis Group, LLC
2 V. E. ARKORFUL ET AL.
Kreslake, & Phalen, 2006), culture and conflicts relative to Hossain, Quaresma, and Rahman (2019) included perso-
the usage of technology for health delivery (Lee, 2014). nal innovativeness in information technology and resis-
Given the relevant, but varying identifications pointed tance to change and sampled 300 participants to
out in prior research, it is important to note that, technology empirically investigate factors driving physicians’ health
adoption is influenced by certain characteristics comprising technology adoption. The study confirmed that social
user-related, technology-specific characteristics, social, cul- influence, facilitating conditions and personal innovative-
tural and economic dimensions. Therefore, being conver- ness in information technology have significant correla-
santwith, and at the same time gaining an understanding of tion with physician’s behavioral intention to adopt
influential factors with regards to health technology adop- technology. However, the study further revealed that per-
tion and utilization is a sine qua non to a successful imple- formance expectancy, effort expectancy and resistance to
mentation of any form of corporate innovation plan and change have no significant influence on technology adop-
strategy, particularly in developing countries where tech- tion. Overall, these studies present heterogenous evidence
nology deployment and usage are at a nascent stage in research findings which could possibly be due to varia-
(Achampong, 2012). This makes health technology adop- tions in methodologies and sample populations.
tion a subject matter worthy of investigation. Part of the Moreover, the study of factors influencing technology
reason is predicated on how such a discourse will help adoption has generated mixed results (Oliveira, Martins,
provide sufficient policy indicators, and at the same time, & Lisboa, 2011; Venkatesh & Davis, 2000). And this could
serve as a guide in devising strategies and interventions plausibly be attributed to complexity of the health-care
geared towards developing an extensive and practical fra- socio-technical systems, attitudinal variations in health-
mework to regulate the use of health technology. care users and actors in other economies industries, as
In the face of the apparent abundant significance the well as the uniqueness of the health-care structure and
technology offers, there is no misgiving regarding the composition (Holden & Karsh, 2010).
pertinence of health technology (Lasker, Humphreys, & The institutional theory has become one of the most
Braithwaite, 2014) especially in promoting universal widely used theory employed to understand organiza-
health coverage. The health-care setting is a highly insti- tional activities, strategies and behaviors (Scott, 2005).
tutionalized and technically complex structure (Scott, Researchers have corroborated that, institutional pres-
Ruef, Mendel, & Caronna, 2000). And considering the sures from corporate institutions environment could
pertinence and urgency of health, and by extension, the significantly exert some degree of influence on organi-
achievement of the inter-connected global goals, more zational activities, strategies, behaviors and the overall
especially universal health coverage, technology deploy- organizational innovation operation process (DiMaggio
ment, adoption and utilization, has recently become a hot & Powell, 1991; Scott, 2005). Considering that technol-
research domain. There is currently a growing body of ogy adoption is mostly an institution-led initiative, it
literature that has extensively discoursed on health tech- becomes not only suitable, but also appropriate to apply
nology acceptance by physicians. Current research on the institutional theory to foster forging a deeper
technology adoption have been widely and largely applied understanding of technology deployment, adoption
to explain adoption intention (Bhattacherjee & Hikmet, and usage. Actually, the institutional theory has been
2007; Ilie, Van Slyke, Parikh, & Courtney, 2009; Sherer, employed for several studies across diverse fields
Meyerhoefer, & Peng, 2016; Terrizzi, Sherer, Meyerhoefer, including operations management (Badewi & Shehab,
Scheinberg, & Levick, 2012; Venkatesh, Morris, & Davis, 2016; Zhang & Dhaliwal, 2009). DiMaggio and Powell
2003). These studies have predominantly focused on indi- (1991) and Scott (2005) assert that, as much as institu-
vidual characteristics and how such factors facilitate or tional pressures may engender significant results, ex
militate against technology adoption. Specifically, in ante heterogeneity factors, amongst others may influ-
a seminal study in Canada using the structural equation ence the generation of various outcomes. Against this
modeling approach, Tsai, Hung, Yu, Chen, and Yen background, investigating practitioners’ technology
(2019) sampled 217 physicians from 15 metropolitan hos- adoption from an integrated perspective composed of
pitals and academic centers to investigate individual tech- technology acceptance and institutional perspective will
nology adoption incorporating three variables comprising be of much importance to scholarship and policy.
perceived service level, self-efficacy and perceived risk. The Specifically, with this research, the study seeks to
study established these variables as significant antecedents investigate ex-ante heterogeneity across practitioners in
to technology adoption. institutions and possible organizational coordination.
In another study employing the partial least square Exploring the effects of these relationships and coordi-
method (PLS) in Dhaka using the Unified Theory of nation will be very essential to understanding practi-
Acceptance and Use of Technology (UTAUT) Model, tioner’s technology adoption which is key to driving
INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 3
health care. Thus, institutional factors and individual In the views of Davis (1989), perceived usefulness
peculiar characteristics may interact well to bring about (PU) refers to the individual’s expectation that using
positive outcomes. Hence, the aim of this research is to a particular technology would increase and enhance his
empirically explore the interaction between the or her work performance (Venkatesh & Davis, 2000;
Technology Acceptance Model and the Institutional Venkatesh et al., 2003). Perceived-Ease-of Use (PEOU),
Theory and their collective effects on driving health- on the other hand, refers to the degree to which a user
centered outcomes. The study will make immense con- anticipates the usage of a technology to be free from
tribution to scholarship by exploring the mediating difficulties or effort. TAM theorizes that PU and PEOU
effect of electronic health innovation technology in sup- determines an individual’s behavioral intention to use
porting universal health coverage which is geared a system. These beliefs act as facilitators between exter-
towards breaking all forms of geographical divide. The nal variables and intention to use (Davis, 1989).
remainder of this paper is structured as follows: Research literature is replete with findings that
Theoretical background and hypothesis discuss relevant underscore the potency of TAM in predicting prospec-
study theories and research hypotheses. Research meth- tive users’ acceptance of technology (Amadu, Syed
ods and data collection shed light on research methodol- Muhammad, Sadiq Mohammed, Owusu, & Lukman,
ogy. Data analysis and results are captured in the data 2018; Sarwar, Zulfiqar, Aziz, & Ejaz Chandia, 2019).
analysis and results section. Discussion and conclusion TAM, widely applied in many industries, has been used
segment focuses on study results. The final section con- to explain the adoption of e-health technology
cludes the research and highlight study limitations and (Bhattacherjee & Hikmet, 2007; Melas, Zampetakis,
implications for future research. Dimopoulou, & Moustakis, 2011; Terrizzi et al., 2012;
Walter & Lopez, 2008), but with mixed results (Holden
& Karsh, 2010). For the purposes of this research, the
Theoretical background and hypotheses study employs perceived-ease-of-use and perceived use-
fulness as study variables….
Technology Acceptance Model (TAM)
The prominent theory used to provide a theoretical fra-
Universal Health Coverage (UHC)
mework to enhance understanding what informs indivi-
dual behavior in adopting and utilizing innovation is the Within the context of reemerging novel universalistic
technology acceptance model. TAM has evolved into an approaches to healthcare, universal health coverage
authoritative model used to explain and predict (UHC) is widely recognized as essential to enhancing
a system’s adoption and utilization (Lee, Kozar, & health, social cohesion, human and economic develop-
Larsen, 2003; Legris, Ingham, & Collerette, 2003; ment (Evans, Marten, & Etienne, 2012). The core compo-
Venkatesh et al., 2003). TAM as a favorite, robust, and nents of the policy encompass accelerating effectiveness,
parsimonious model for predicting user acceptance high-quality services and population coverage (WHO,
(Venkatesh & Davis, 2000) constitutes an important 2017). As a catalyst for change with a massive prospect
determinant of technology acceptance and use behavior. for efficiency and equity, the world health organization
The objective of TAM is to examine how users’ attitudes referred to UHC as “the single most potent concept public
and beliefs influence their acceptance or otherwise of health has to offer” (WHO, 2017). Health structures there-
innovation. Its efficacy in determining the prospects of fore owe it a responsibility to provide universal and equi-
innovation, relative to predicting acceptance or rejection table access to health services to further ensure improved
has made its usage a popular one. TAM will foster outcomes (Ghebreyesus, 2017) as it is linked to other
understanding of innovation, including examination of sectors and expressly, expected to foster healthy sustain-
social systems and individual function within the system able development. As an integrated approach to improv-
(Rogers, 2002). In TAM, derived from the Theory of ing health outcomes, UHC embraces the whole health
Reason Action (TRA) from the field of social psychol- system and puts rights and equity at the center of its
ogy, Fishbein and Ajzen (1975) proposed systems usage vision. Implicit in the definition of UHC is equity, access,
as a reaction that can be anticipated by user’s stimulus and inclusiveness (World Health Organization &World
based on the competencies and structures of actual exist- Bank, 2017). It is therefore essential that health systems
ing systems (Chuttur, 2009). Dwelling on previous TRA integrate e-health with UHC to address inherent systemic
related works, Fishbein and Ajzen (1975), Davis (1989) inequalities. In this regard, TAM, alongside the
segmented and simplified the TRA attitude related con- Institutional Theory, are used to measure health practi-
structs into two (2) broad constructs: Perceived tioners’ adoption and utilization of electronic health tech-
Usefulness (PU) and Perceived-Ease-of-Use (PEOU). nology in promoting UHC in Ghana.
4 V. E. ARKORFUL ET AL.
The Institutional Theory (IT) need to maintain good cooperative relationship with
regulatory authorities. Resisting such changes may
The Institutional Theory which has received prominent
damage relations between subordinate health institutions
academic usage has widely been used in studies on
and superordinate regulatory authorities. From the fore-
organizational technology innovation adoption and
going discourse, the research designates relevant tech-
practices. (Dimaggio, 1991; DiMaggio & Powell, 1983;
nology infrastructure as a coercive force.
Meyer & Rowan, 1977; Tolbert & Zucker, 1983).
Mimetic forces are borne out of pressures to copy or
Institutional theorists are of the view that modern
emulate other organizations' activities, systems or struc-
social structures and institutions as complex and com-
tures. Dimaggio (1991) posit that “once a field becomes
plicated organizations are seen as systems of rationally
well established, there is an inexorable push toward
ordered rules and activities where the ready acceptance
homogenization”. It is easier for organizations to transact
of policies and practices is pivoted on legitimate and
with other organizations, to attract career-minded staff
rational means to attain organizational goals. This pre-
acknowledged as legitimate and reputable. Some organi-
supposes that, institutions, even including health dis-
zations “copy” or imitate other organization’s model
pensing systems, are susceptible to changes (Meyer &
(structure, process, or forms) whenever deemed success-
Rowan, 1977; Scott et al., 2000).
ful. Some mimetic behaviors are sometimes inadvertent
Institutional theorists also contend that the institu-
and unplanned and may happen during staff transfer or
tional environment can strongly influence structures.
turnover, or from consultant inputs. The desire to emu-
(Dimaggio, 1991; Gibbs & Kraemer, 2004; Orlikowski &
late can significantly impel institutions to provide train-
Barley, 2001; Scott et al., 2000). Institutional effects are
ing for personnel. Mimetic forces elucidate on the
dispersed across mimetic, normative, and coercive iso-
widespread adoption of, for example, management prac-
morphism. (DeNavas-Walt, Proctor, & Smith, 2013;
tices (Abrahamson, 1996).
DiMaggio & Powell, 1983). The institutional theory
This logic can be extended to technology adoption
model has been applied to explain adoption of technolo-
by health practitioners. Technology benefits users by
gies and enterprise applications (Liang, Saraf, Hu, & Xue,
helping to increase organizational dexterity and perfor-
2007; Soares-Aguiar & Palma-dos-Reis, 2008), e-com-
mance. Within the corporate health sector, actors are
merce and supply chains (Gibbs & Kraemer, 2004;
likely to imitate competitors whenever the advantages
Jeyaraj, Balser, Chowa, & Griggs, 2004).
and benefits of technology deployed by other users are
In the views of (Dimaggio, 1991), coercive pressures
realised. And by so doing, health actors are likely to
resulting from both formal and informal sources are
seek for adequate practitioner training on the use of the
placed on organizations by other organizations upon
technology, so as to gain the requisite knowledge to
which they are dependent on, in the broader context of
ably utilize technology to improve outcomes. In this
the society within which these organizations function.
view, adequate practitioner training, within the
In other words, coercive pressure emanates from
research model represents a mimetic force.
authorities or other organizations that have power
Normative pressures emanate from the collective
over other organizations. This pressure comes into
expectation of players within a particular organiza-
force when organizations are under compulsion to
tion. These expectations, likely to stem from suppliers
adopt structures or rules. Coercive pressure may come
and competitors may develop into corporate stan-
from government, parent company, official governing
dards, norms and conventions (Scott, 2005) and
or regulatory agency, partners and customers.
may further induce normative effects on institutions
Organizations seem more susceptible to coercive influ-
to act in line with contemporary corporate practices
ences. This influences technology adoption. In the case
since they are in sync with legitimacy (Scott, 2005).
when innovation is needed to fulfill some requirements,
Institutions by nature are dynamic. This institutional
organizations often have little or no other option than
dynamism exerts pressures on institutions and
adopting. On this basis, the existence of relevant infra-
enables them to act in line with shared standards
structures and accessories within organisational settings
and norms within the corporate space (Berrone,
tend to influence technology adoption.
Fosfuri, Gelabert, & Gomez-Mejia, 2013). Given this
In the case of health technology adoption and utiliza-
situation, change resisting, non-complying actors or
tion by practitioners, developments in other health sec-
sector players are likely to be jettisoned. As such,
tors in different settings or geographical areas may push
actors are expected to comply. For this reason, man-
authorities to implement appropriate strategies like put-
agerial heads may tend to conform and support tech-
ting in place relevant technology infrastructure. As such,
nology adoption practices. Accordingly, practitioners’
health outfits are more likely to comply because of the
adoption will be driven by networks including
INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 5
management which contributes to establishing an H7: PU of e-health technology has a positive relation-
institutional or organizational norm. In the light of ship with EH Adoption
the above, the research proposes management support
as a normative pressure. H8: MS of e-health technology has a positive relation-
ship with EH adoption
Electronic health
H9: MS of e-health technology has a positive relation-
Studies over the years have buttressed the overwhelming ship with UHC
contribution of innovation adoption and utilization. It is
in this respect that the potency of the innovation’s inte- H10: RTI has a positive relationship with e-health tech-
gration into the health sector and its contribution to nology adoption
universal health coverage is investigated. Bossen, Jensen,
and Udsen (2013; Khalifehsoltani et al., 2010) in a study H11: E-health as a mediator influences the relationship
attest that electronic health does not only improve but between PEOU, PU, MS, APT, RTI and UHC
also strengthens the quality of care given to patients.
Chaudhry et al. (2006) also confirm the significance of
technology in health-care delivery. According to Agrawal
Research methods and data collection
(2002); Khan, Shahid, Hedstrom, & Andersson (2012),
e-health innovation improves productivity and efficiency The study employs a Structural Equation Modeling
in healthcare delivery. Similarly, evidence abounds that approach to develop and verify the research model
e-health tools have a positive effect on users (Bedeley & and to illustrate the relationship between the constructs
Palvia, 2014). It thus improves efficiency and results in (Figure 1). The survey was created online using www.
enhancing behavioral outcomes as compared to non- wjx.cn and the questionnaire link sent to participants
users (Murray, Burns, Tai, Lai, & Nazareth, 2004). via WhatsApp, Facebook, Mails, and WeChat. Also, to
Furthermore, as observed by (Burton, Anderson, & help reach out to respondents who for reason(s) of;
Kues, 2004), health innovation advances an improved
level of communication and facilitates overall improved (i) Not being users of any of the questionnaire
coordination of healthcare (Bodenheimer, Wagner, & administration mediums utilized;
Grumbach, 2002). (ii) Not being regular users of the questionnaire
administration/dissemination platforms used;
(iii) Encountering or likely to experience internet
Hypotheses
network challenges, paper questionnaires were
From the above discussions, the following hypotheses administered in addition to the online surveys.
are formulated for the various study constructs;
The study was conducted among actors in the health
H1: PEOU of e-health technology has a positive rela- sector, including Nurses, Medical doctors, Health
tionship with UHC Administrators and other health support service provi-
ders like pharmacists, ambulatory service providers, and
H2: PU of e-health technology has a positive relation- laboratory technicians. Therefore, practitioners from
ship with UHC. across diverse fields of expertise in different health facil-
ities across Ghana were targeted under convenient sam-
H3: MS of e-health technology has a positive relation- pling technique. To avoid bias, participants were not
ship with UHC offered any incentive. In this study, a total of 450 ques-
tionnaires were distributed. At the end of the survey
H4: APT on e-health technology has a positive relation- which targeted fully engaged practitioners in both govern-
ship with UHC ments and non-government owned and controlled health
facilities, 420 questionnaires were retrieved. After sorting
H5: RTI of e-health technology has a positive relation- out questionnaires with incomplete responses, 416 use-
ship with UHC able questionnaires representing 92.4% were used. This is
an indication of a high response rate and internal validity
H6: PEOU of e-health technology has a positive rela- for the study. The demographic features of respondents
tionship with EHT are summarized in Table 1. The survey involved 40 items
6 V. E. ARKORFUL ET AL.
Figure 1. Conceptual Framework.
PU = Perceived Usefulness; PEOU = Perceived-Ease-of-Use; MS = Management Support; APT = Adequate practitioner training; RTI =
Relevant technology Infrastructure; EHT = Electronic Health Technology; UHC = Universal Health Coverage.
Table 1. Descriptive information of samples demographic disseminated over chosen constructs adapted from pre-
characteristics. viously tried, tested and validated questionnaires. The
Measures Frequency Percentage researchers made few changes in the language of the
Gender
Male 172 41.3 questions to reflect the measurements of the constructs.
Female 244 58.7 The items for Perceived-Ease-of-Use (PEOU) were
Age
>30 76 18.3 adapted from prior studies, who have already established
30-39 148 35.6 their reliability and validity (Davis, 1989; Morton, 2008;
40-49 124 29.8
50-59 56 13.5 Rauniar, Rawski, Yang, & Johnson, 2014; Sanch, Cortijo,
60+ 12 2.9 & Javed, 2014; Sarwar et al., 2019). Perceived Usefulness
Educational Level
Certificate 28 6.7 was also adapted from (Davis, 1989; Morton, 2008;
Diploma 120 28.8 Rauniar et al., 2014; Sanch et al., 2014; Sarwar et al.,
Bachelors 124 29.8
Master 84 20.2 2019). Management Support was adapted from
PhD 56 13.5 (Aldosari, 2004; Dansky, Gamm, Vasey, & Barsukiewicz,
Others 4 1.0
Years of Service 1999; Morton, 2008). Similarly, Adequate Practitioner
>5 100 24.0 Training was adapted from Morton (2008) whiles
5-10 220 52.9
11-15 96 23.1 Relevant Technology Infrastructure was also adapted
15+ 0 0
Area of Expertise from Bultum (2014). Constructs for measuring
Medical Doctor 92 22.1 Electronic Health were adapted from prior studies by
Nurse 180 43.3
Health Administrator 64 15.4 Morton (2008). Finally, the items used for Universal
Others 80 19.2 Health Coverage were adapted from (WHO, 2017).
INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 7
A 5-point Likert scale ranging from Strongly Table 3. Analysis of variable loadings.
Disagree to Agree Strongly was used to measure the Constructs OL VIF Q2 R2 AVE rho A
responses. Specific changes were made in sentence APT1 0.909 2.246 0.001 0.703 0.825
APT2 0.867 2.672
structure of questions according to the current study. APT3 0.712 2.294
To help develop an understanding of the study, a brief APT4 0.853 2.087
EHT1 0.916 2.697 0.732 0.997
was held for respondents about the study purpose. In EHT2 0.920 2.753
compliance with research ethics, respondents were EHT3 0.782 2.351
EHT4 0.796 2.382
assured of the confidentiality of all information pro- MS1 0.773 2.258 0.028 0.053 0.617 0.905
vided. They were also guaranteed that data collected MS2 0.762 2.174
MS3 0.820 2.296
will be used only for academic purpose. MS4 0.838 2.441
MS5 0.818 2.357
MS6 0.752 2.111
MS7 0.727 2.000
Data analysis and results PEOU1 <- 0.844 2.454 0.002 0.004 0.71 0.902
PEOU2 <- 0.866 2.891
Based on the proposed theoretical framework and hypoth- PEOU3 <- 0.829 2.496
PEOU4 <- 0.841 2.447
eses, Structural Equation Modeling technique along with PEOU5 <- 0.833 2.216
PLS-SEM data analysis software version 3.0 was used for PU1 0.760 2.507 0.008 0.018 0.657 0.941PU2 0.821 2.423
data analysis and establishing the model. In analyzing the PU3 0.851 3.579
data, measurement models were initially verified to con- PU4 0.759 2.658PU5 0.859 2.882
firm the validity and reliability of the study constructs. PU6 0.854 2.310
Subsequently, the structural model was assessed by using PU7 0.761 2.036RTI1 0.898 6.145 0.008 0.014 0.694 0.87
hypotheses testing. The proximate reason for the use of RTI2 0.910 6.424
SEM is to use an observed variable to measure an unob- RTI3 0.681 1.389RTI4 0.824 1.738
served variable. In analyzing the data, the measurement UHC1 0.788 1.861 0.044 0.085 0.659 0.89
UHC2 0.767 1.948
models were verified to confirm the validity and reliability UHC3 0.856 2.422
of the constructs using SPSS v.23. And finally, structural UHC4 0.851 2.521
UHC5 0.794 1.642
modeling was used to measure the mediating effect of
OL = Outer loadings, VIF = Variance Inflation Factor, Q2 = Collinearity
electronic health. Discriminant validity analysis was con- redundancy, R2 = Explanatory power, AVE = Average variance extract,
ducted to assess the degree of correlation among the latent rho_A = Unidimensionality of Reliability.
variables. The results of the discriminant validity analysis
indicate that the indices according to benchmark > .7 power. (APT = 0, MS = 0.053, PEOU = 0.004, PU = 0.018,
validate the proposedmodel. FromTable 2, the highlighted RTI = 0.014, UHC = 0.085). The measurements are against
diagonal line is an indication of the relationship of the the benchmark which indicates that, values between .19-.33
latent variables which is also the square root of their (weak) .33-.57 (Moderate) and >.67 strong (Hair, Black,
respective average variance extract (AVE) captured in the Babin, & Anderson, 2010).
outer loadings in Table 3. Also, composite reliability (CR)
analysis was conducted to confirm the reliability and valid-
ity of relationships between latent variables in the study of
Discriminant validity analysis
health innovation adoption and utilization. In the analysis,
all composite reliability scores of our variables were found Model analysis
to be above .7 showing sufficient statistical significance of In the reliability test, Cronbach alpha values were
proposed constructs (Fornell & Larcker, 1981; Hair, found to be more than .7, indicating a reasonable
Anderson, Tatham, & Black, 1998;Wu, 2010). As indicated scale of reliability. Table 2 and Figure 2 capture the
in Tables 2 and 3, R square recordings showweak construct results. Path coefficient of the various constructs in
Table 2. Fornell-Larcker criterion of discriminant validity.
CONSTRUCTS APT EHT MS PEOU PU RTI UHC CR R Square Cronbach Alpha
APT 0.839 0.904 0 0.883
EHT 0.007 0.856 0.916 0.888
MS 0.108 0.230 0.785 0.918 0.053 0.896
PEOU 0.061 0.061 0.161 0.843 0.925 0.004 0.898
PU −0.012 0.133 0.088 0.175 0.810 0.930 0.018 0.916
RTI 0.119 0.120 0.123 0.094 0.050 0.833 0.900 0.014 0.849
UHC 0.101 0.061 0.090 0.253 0.042 0.140 0.812 0.906 0.085 0.872
APT = Adequate practitioner training, EH-Electronic Health, MS = Management Support, PEOU = Perceived-Ease-of-Use, RTI = Relevant technology
Infrastructure, UHC-Universal Health Coverage, CR-Composite Reliability.
8 V. E. ARKORFUL ET AL.
Figure 2. Measurements and Structural Model Equation.
PU = Perceived Usefulness; PEOU = Perceived-Ease-of-Use; MS = Management Support; APT = Adequate practitioner training; RTI =
Relevant technology Infrastructure; EHT = Electronic Health Technology; UHC = Universal Health Coverage.
Table 4, alongside their respective hypotheses, indi- (Fornell & Larcker, 1981; Hair et al., 2010) as shown
cates the significance of inferences. The external in Table 3.
loadings, on the other hand, recorded significant Next, partial least square (PLS) analysis was con-
loadings which were all above the threshold of .7. ducted to ascertain values of Variance Inflation Factor
Table 4. Results of hypotheses testing.
Effects
HYP Original Sample (O) Sample Mean (M) Standard Deviation t f2 Indirect(β) p < value inference
H1PEOU->UHC 0.234 0.236 0.081 2.882** 0.057 0.234 0.004 Supported
H2PU-> UHC −0.01 −0.008 0.062 0.156 0 −0.01 0.876 Unsupported
H3MS-> UHC 0.026 0.035 0.049 0.538 0.001 0.026 0.591 Unsupported
H4APT->UHC 0.072 0.055 0.095 0.757* 0.005 0.072 0.449 Unsupported
H5RTI->UHC 0.103 0.107 0.058 1.766** 0.011 0.103 0.078 Supported
H6PEOU->EHT 0.061 0.068 0.053 1.159** 0.004 0.061 0.247 Supported
H7PU->EHT 0.133 0.153 0.066 2.024*** 0.018 0.133 0.044 Supported
H8MS->EHT 0.23 0.239 0.068 3.362*** 0.056 0.230 0.001 Supported
H9APT->EHT 0.007 0.004 0.071 0.092 0 0.007 0.927 Unsupported
H10 RTI->EHT 0.12 0.125 0.06 1.995*** 0.015 0.12 0.047 Supported
H11EHT->UHC 0.029 0.026 0.036 0.796* 0.001 0.029 0.427 Supported
PEOU = Perceived-Ease-of-Use; PU = Perceived Usefulness; MS = Management Support; APT = Adequate Practitioner Training; RTI = Relevant Technology
Infrastructure EH = Electronic Health; UHC = Universal Health Coverage; β = path coefficient, t = significance, f2 = effect size, p value *t is sign at p > .05, **t
is sign at p > .01 and ***t is sign at p > .001.
INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 9
(VIF) between the latent variables. From the results recorded (β = 0.072, SD = 0.095, f2 =0.005, t2 = 0.757,
obtained, the values were sufficiently consistent with p > .449) and showed an insignificant relationship. As
the standard measurement of .5 (Hair et al., 2010). such, the hypothesis was unsupported. The findings
Regarding external measurement loadings, which mea- further recorded under H5 that RTI – > UHC (β =
sures the relationships between the unobserved construct 0.103, SD = 0.058, f2 = 0.011, t2 = 1.766, p > .078). This
and errors, the values obtained were reliable and proved supported the hypotheses. Besides, H6; PEOU > EHT
consistent with Dillon-Goldstein’s rho which also mea- recorded (β = 0.061, SD = 0.053, f2 = 0.004, t2 = 1.159,
sures Unidimensionality of the reliability which is better P > .249).
in principle than Cronbach alpha (Chin, 1998). From From the inference, the hypothesis is significant to the
Table 3, it is clear that the values obtained were all study of health innovation technology adoption and uti-
beyond the minimum threshold of .7 (Hair et al., 2010). lization studies. It was also revealed that H7; PU -> EH
recorded (β = 0.133, SD = 0.066, f2 =0.018, t2 =2.024,
p > .044). This shows a significant positive relationship.
Hypotheses testing and effects Per the analysis of results obtained, H9; APT – > EH
After signifying the validity of the measurement model, (β = 0.007, SD = 0.071, f
2 = 0.000, t2 = 0.092, p > .927)
proposed hypotheses were tested. Figure 3 indicates path was unsupported. The results of H10; RTI – > EHT
analysis results using PLSmarts software v.3.0. The results (β = 0.120, SD = 0.060, f
2 =0.015, t2 =1.995, p > .047)
found H1; PEOU – > UHC (β = 0.234, SD = 0.081, indicated that the existence of relevant technology infra-
f2 = 0.057, t2 = 2.882, p > .004) as supported and con- structure is a sufficient predictor of practitioners’ inten-
firmed that perceived-ease-of-use of e-health technology tion to adopt and utilize ehealth technology innovation.
has a significant relationship which could be mediated by This confirmed the proposition. Finally, as captured in
electronic health technology adoption and utilization. H11; EH – > UHC (β = 0.029, SD = 0.036, f
2 = 0.001, t2 =
Under H2; PU – > UHC (β = 0.01, SD = 0.062, 0.796, p > .427), a weak, yet significant relationship was
f2 = 0.000, t2 = 1.156, p > .876), the study results revealed recorded. This supported the hypotheses. Results indicate
no significant relationship between perceived usefulness that all proposed hypotheses supported but H2, H3, H4
of ehealth technology adoption and utilization as and H9.
a predictor of universal health coverage achievement.
Also, under hypothesis 3; MS -> UHC (β = 0.026,
SD = 0.049, f2 = 0.001, t2
Mediating effect of electronic health
= 0.538, p > .591), results
revealed no significant relationship. As such, the hypoth- Integral to the objectives of this study, how electronic
eses were unsupported. Moreover, H4; APT- > UHC health as a mediating variable can influence the
Figure 3. Structural Equation Modeling with Results of Significance.
PU = Perceived Usefulness; PEOU = Perceived-Ease-of-Use; MS = Management Support; APT = Adequate practitioner training; RTI =
Relevant technology Infrastructure; EHT = Electronic Health Technology; UHC = Universal Health Coverage.
10 V. E. ARKORFUL ET AL.
relationship between the independent variables; thus, practitioners’ non-conversance with health innovation
PEOU, PU, MS, APT, RTI and the dependent variable; adoption and perhaps the newness of the goal of uni-
thus, UHC is investigated. With particular emphasis on versal health coverage. This inconsistency could further
the path coefficient showing the relationship between be attributed to the complexity of the institutionalized
the mediator and dependent variable, as shown in health structure which has different actors with diverse
Figure 3, EH – > UHC recorded (β = 0.029, SD = attitudes and behaviors towards work. Part of the ways
0.036, f2 = 0.001, t2 = 0.796, p > .427) which shows to forge a much stronger consensus towards driving the
a positive relationship as well as the predictive power of achievement of UHC goal by 2030 calls for a much
the mediator to influence the dependent variable. To broader stakeholder engagement on the subject matter.
the best of the researchers’ knowledge, there is Similarly, there was an insignificant relationship
a literature deficit relative to universal health coverage between management support of electronic health tech-
owing to the newness of the global goal. And this nology as a predictor of driving the achievement of
constitutes part of the reason why they will make semi- UHC goals. This result is not supportive of hypothesis
nal contributions to health technology-based studies 3. This results in dissonance with (Aldosari, 2004;
while breaking grounds for further future research. Dansky et al., 1999; Morton & Wiedenbeck, 2009;
Figure 2 indicates the mediating critical factor and Poon et al., 2004) who confirmed management support
path coefficient values regarding the interaction effect of innovation usage as a predictor of health technology
of independent and dependent variables. Results adoption. Therefore, it becomes apparent that manage-
revealed EHT to be mediating the relationship between ment-led initiative is less likely to yield results. In the
the independent and dependent variables as shown in views of the researchers, adoption and utilization of
Table 3, Figure 1. innovation to driving UHC goals rest more on indivi-
dual practitioners. In this regard, the adoption endea-
vor may have to be pushed and pursued in a manner
Discussion and conclusion transiting from the individual practitioners’ level and
Our findings indicate that perceived ease of use of then to a larger level to involve management. Thus,
electronic health technology, as captured in the results, from micro to macro level. The insignificant effect of
have a significant effect in relations to driving universal management support on universal health coverage
health coverage. This implies that users’ comfort and should be a wakeup call for health establishments to
convenience in the adoption and utilization of engage professional health administrators with the
a particular health technology, to a greater extent, requisite qualification and understanding of the health
serves as a determining factor for practitioners. sector. Engaging human resources with the requisite
Therefore, in pursuit of universal health coverage knowledge and skills would help practitioners appreci-
goals, it is important that factors of convenience are ate the goals and pursue them more rigorously and
integrated into the innovation design framework to vigorously.
enhance widespread usage. With regards to prior stu- Furthermore, hypothesis four was also unsupported.
dies on how ease of use could impact on UHC drive, to The study results revealed an insignificant relationship
the best of the researcher’s knowledge, there is a dearth between adequate practitioner training on electronic
of literature on this subject matter. This however said health technology adoption and utilization and UHC
(Morton, 2008; Sarwar et al., 2019) in a study confirm goal drive. This could be interpreted to mean that
the positive effect of perceived ease of use on the training offered to practitioners has or could have little
adoption of a technology. or no significance on technology adoption. While train-
Regarding H2, the study findings indicate that per- ing on electronic health innovation appears to be
ceived usefulness of adoption and utilization of an important to ensure not only adoption and utilization
innovation by a user or users has an insignificant cor- but also to propelling UHC goals, it does however
relation with driving UHC goals. This was inconsistent appear not to have an overall impact. The varying
with the proposed hypothesis. However, in the assump- attitudes of practitioners towards purposefully driving
tions of the authors, this could be ascribed to the innovation are confirmed by Teach and Shortliffe
complex nature of the UHC goal drive using technol- (1981). In the views of the researchers, the insignifi-
ogy. Under this circumstance, the study concludes that cance could be attributed to insufficient motivation to
the UHC policy goal requires more than technology to use technology and anxiety brought about by technol-
be driven (Adams et al., 2013). The insignificant effect ogy use (Morton, 2008). Practitioners’ familiarity with
of health innovation adoption and utilization on uni- the conventional health-care process in Ghana which
versal health-care drive could also be attributed to does not involve much utilization of technology in
INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 11
dispensing service is also likely to be a reason for the electronic health technology. While training seems to be
low premium, priority and significance placed on train- very important to practitioner’s technology adoption, it
ing as a conduit to equip practitioners with the requi- appears to have no impact on adoption. This finding is
site skills in the use of technology to drive UHC goals. consistent with (Dillon & Morris, 1996; Hurley, 1992;
Despite the negative relationships recorded, our study Karsh, 2004; Morton & Wiedenbeck, 2009).
revealed a significant correlation between the existence As confirmed in prior studies by Grimson, Grimson,
of a relevant technology infrastructure as an adoption and Hasselbring (2000), the results of H10 indicated
stimulus to drive UHC. This supported the hypotheses that the existence of relevant technology infrastructure
and indicated that relevant technology infrastructure for is a sufficient determinant of practitioners’ intention to
e-health technology has an effect on universal health adopt and utilize ehealth technology innovation.
coverage. Prior studies like (Bultum, 2014) confirm Fundamentally, technology infrastructure comprises
how the existence of a relevant technology infrastructure the existence of technology-driven communication
can influence technology adoption, utilization and ser- devices like computers, which are essential to impelling
vice delivery. This is a confirmation of hypotheses 5. health technology innovation adoption and utilization
Besides, the results of analysis further revealed that by practitioners and consumers alike. The pertinence of
perceived ease of use recorded a positive relationship this infrastructure lies in its strength to provide con-
with electronic health innovation adoption and utiliza- nectivity as a basis for enhancing and improving health
tion. From the confirmed inference 6, the hypothesis is service delivery whiles overcoming its inherent system-
supported. This is consistent with (Amadu et al., 2018; related challenges (Detmer, 2003).
Davis, 1989; Sarwar et al., 2019). In confirmation of H7, More specifically, a relevant technology infrastructure
it was revealed that users’ perceived usefulness of elec- constitutes the foundation on which sound clinical and
tronic health innovation technology has a significant clerical decision-making efforts of practitioners subsist.
correlation with adoption. The study indicated a strong This comes with series of support for applications whiles
correlation between the two variables. This is consistent integrating the systems' applications to solicit for accurate
with seminal information system-based studies such as and complete data. Relevant Technology Infrastructure
Chau & Hu (2002); Chismar & Wiley-Patton (2003). (RTI) according to the findings of this study has
Aldosari (2004) and Seligman (2001) also corroborate a significant predictor relationship with electronic health
the significance of perceived usefulness to innovation innovation. By inference, it appears that the existence of
adoption. This finding is an indication of how percep- a relevant technology infrastructure can influence electro-
tions of user unfriendliness or otherwise could influence nic health technology innovation adoption and utilization
practitioner’s intention or decision to adopt technology. by health practitioners. The significant correlation between
Furthermore, results under hypotheses 8 revealed technology infrastructure and health innovation technol-
a significant relationship between Management Support ogy adoption is confirmed by (Grimson et al., 2000).
and electronic health technology adoption intention. Finally, as captured in H11, electronic health innova-
Management support which refers to practitioners’ expec- tion as a mediator between the independent and depen-
tation and perceptions of management’s ability to provide dent variables recorded a coefficient = 0.029*, indicating
adequate resources to enhance electronic health innova- a significant mediating effect. Even though the signifi-
tion adoption and utilization (Anderson & Aydin, 1997; cance is weak, other research studies (Fabrizi, Guarini, &
Lorenzi & Riley, 2000) and providing moral and financial Meliciani, 2018) confirm the significance of the results.
support, training programs, and clinical and technical Hence, the hypothesis is significantly supported.
support (Simon et al., 2007) is confirmed as a predictor From the discourse, it could be deduced that
of health practitioners’ adoption and utilization intention technology can be an excellent tool and driver of
of innovation. This is confirmed by (Aldosari, 2004; health service delivery, whiles helping to break geo-
Morton & Wiedenbeck, 2009; Poon et al., 2004; Wu & graphical barriers, ensuring equity, promoting effi-
Lee, 2005). The study results indicate the need for insti- ciency and quality in healthcare delivery and
tuting a strong management that can provide the building a sustainable and resilient healthcare sys-
resources necessary to enhance adoption and utilization. tem to help promote the larger goal of universal
Analysis of data revealed that adequate practitioner health coverage. In this regard, the attitude of prac-
training, as captured under hypothesis 9 revealed titioners, who constitute the nucleus of the health
a negative relationship with user adoption and utilization system, and as such drivers of the UHC policy
of electronic health technology. Against this backdrop, it vehicle become central in this effort as the nuances
is obvious that adequate practitioner training is not suffi- in practitioners’ attitude relative to technology
cient and enough predictor of physicians’ adoption of adoption and utilization is essential. For this reason,
12 V. E. ARKORFUL ET AL.
practitioners need to be committed and motivated that accommodate the different specialties, users’ technol-
to realize the goal, while improving professional ogy efficacy levels and needs.
capacity through education and training. From the Finally, this study has certain limitations. First, this
results of the various hypotheses indicated and research is purely quantitative; the data for this study
explained above, as captured in Table 4, except for were collected through survey questionnaires. Also, with
H2, H3, H4 and H9, all hypotheses indicated regards to this study, medical practitioners from across the
a significant relationship. various fields of expertise in both government and private
Summarily, this study concludes that technology has health facilities in Ghana were interviewed. Participants
permeated diverse facets of our social life. This is amply extracted from both rural and urban, small, medium and
evidenced in its enormous development, subsequent large health facilities were considered for data collection.
deployment and utilisation in institutions and sectors On this score, future research could be also be conducted
including the health sector. The integration of technology by focusing on specific expertise in a specific government
is substantial and has proven to be very beneficial in health or private health facility. The study also recommends that
service delivery as it aids practitioners in discharging their future research focus on health practitioners and facilities
duties effectively and efficiently. In spite of this, the adop- in different geographic locations. Undertaking this
tion of technology and its utilisation as evidenced in prior research will help to investigate variations in factors such
studies, and more precisely in this study, appear to be as practitioners’ field of practice, facility type and resour-
influenced by a concatenation of factors. As such, technol- cefulness, size and geographical location and their rela-
ogy adoption attitudes could be varying and chiefly tionship with technology adoption and utilization. This is
informed and influenced by different elements. The cur- another research to be commissioned. Follow-up studies
rent study specifies that PEOU, PU, MS, APT and RTI are with focus groups, user interviews, or observations would
key positive factors to influencing technology adoption and also help provide a more detailed understanding of factors
utilization by practitioners. The analysis further revealed influencing practitioners’ innovation adoption and needs.
that electronic technology innovation adoption, which Future research should also be undertaken using qualita-
within the purview of the topic under investigation – tive research method. Furthermore, the study also recom-
ehealth and its potential impact to driving Universal mends future studies to among other things, focus on
Health Coverage status appeared significantly supported factors affecting electronic health technology adoption by
as hypothesized. In the world of information technology, patients across different social spheres. Lastly, the research
given the urgency of health matters, and the unbridled suggests future studies to further concentrate on exploring
global commitment to asserting equity and quality in how technology could effectively and efficiently be har-
health-care delivery, building a responsive, sustainable nessed and sufficiently deployed to help in expeditiously
and resilient health-care system in promoting sector effec- pushing the agenda of universal health coverage.
tiveness and efficiency, having a deeper insight into the
complexities and intricacies underpinning technology
adoption and utilisation behavior of users is crucial for Conflict of interest
technology adoption and utilisation in the overall global The authors declared no potential conflicts of interestwith respect
stride to achieving universal health coverage. to the research, authorship, and/or publication of this article.
Future implications and limitations Ethical approval
Health sector practitioners can use electronic health tech- All procedures performed in this study are in accordance
nology innovation to promote efficiency, effectiveness and with the ethical standards of the institutional and/or national
research committee and with the 1964 Helsinki Declaration
overall, improve health sector performance to the benefit of and its later amendments or comparable ethical standards.
not only patients but also practitioners as well. Practitioners
can develop high creativity among themselves by promot-
ing innovation which is likely to help global health care and Informed consent
delivery. The results of this study can support individual Informed consent was obtained from all individual partici-
and institutional stakeholders to gain a better understand- pants included in the study.
ing of factors (direct and indirect, explicit and implicit) that
is likely to inform adoption and utilization of electronic
health technology by health practitioners in the global effort Funding
to advancing and achieving universal health coverage. Also, The authors received no financial support for the research,
stakeholders can use the study findings to develop systems authorship, and/or publication of this article.
INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 13
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16 V. E. ARKORFUL ET AL.
Appendix 1. MS 1 Electronic health is vital to top management.
MS 2 Electronic health will be introduced to me force-
A brief overview of the study fully by management.
The purpose of this study seeks to investigate technology MS 3 Management will support by proving the needed
integration in healthcare delivery in Ghana and assess its resources during implementation of electronic health
potency in not only driving the health sector but also helping technology.
to achieve universal health coverage (UHC) through the MS 4 Management will involve me in the implementa-
mediating role of electronic health technology (ehealth). tion of electronic health technology.
Health technology integration (ehealth) within the precincts MS 5 Management will provide me the required training
of this study is defined as thedeployment of Information and needed to use electronic health technology effectively.
Communication Technology (ICT) into the health service MS 6 I will have easy access to resources to help me
and providing infrastructure for improving health and understand and use electronic health.
healthcare of people. This may encompass the use of online MS 7 Management expects me to use electronic health
information systems by health-care professionals and hospi- technology innovation.
tal services, mobile health telemedicine services and other Adequate Practitioner Training (Morton, 2008)
technology-driven healthcare support services. By universal APT 1. The training I will receive on the electronic
health coverage, we infer to the principle whereby healthcare health will be sufficient.
delivery and the building of related infrastructure are cen- APT 2 I will accept the training that I need to be able to
tered on equity, quality, responsiveness, efficiency and resi- understand and use electronic health technology.
lience whiles breaking all divide gaps. Please provide your APT 3 The electronic health training will be more
honest response as per the scale. useful to my work.
APT 4 The electronic health training will make it easier
Constructs and measurement items: for me to use health technology.
Perceived-Ease-of-Use (Davis, 1989; Morton, 2008; Relevant Technology Infrastructure (Bultum, 2014)
Rauniar et al., 2014; Sanch et al., 2014; Sarwar et al., RTI 1 Poor internet connection is not good enough to
2019). support electronic health.
PEOU 1 My interaction with electronic health will be RTI 2 ICT infrastructure availability will help electronic
clear and understandable - “User-Friendly.” health technology.
PEOU 2 Learning to use electronic health technology RTI 3 Electronic health technology may not perform
will be easy for me. well because of technology network challenges.
PEOU 3 I expect to become skilled in using electronic RTI 4 Electronic health technology infrastructure is too
health technology. expensive to be provided.
PEOU 4 I expect electronic health technology to be Electronic Health (Morton, 2008)
accessible for health practitioners to use. EH 1 I prefer use of personal computer (laptop or
PEOU 5 Electronic health is flexible to interact with it. handheld) device in my work.
Perceived Usefulness (Binesh, 2018; Davis, 1989; EH 2 The frequency of use or familiarity with electronic
Morton, 2008; Rauniar et al., 2014; Sanch et al., 2014) health is an essential component of its usage.
PU 1 Using electronic health will improve the quality of EH 3 I.C.T training or experience is a good determi-
work and services provided to patients. nant in e-health technology adoption.
PU 2 Using electronic health will give me greater con- EH 4 I.C.T knowledge will enhance technology use in my
trol over my work. work.
PU 3 Using electronic health will allow me to work Universal Health Coverage (World Health
quickly and increase productivity. Organization, 2017)
PU 4 Using electronic health will allow me to accom- UHC 1 Electronic health Technology integration will
plish more task and improve learning performance. promote and ensure equity in healthcare.
PU 5 Using electronic health will enhance effectiveness UHC 2 The integration of electronic health care will
in my job. enhance quality in healthcare delivery.
PU 6 Using electronic health technology will make my UHC 3 Electronic health technology will help in build-
job easier to perform. ing responsive health system.
PU 7 Electronic health will be a useful tool for practi- UHC 4 Electronic health technology integration will
cing my profession. promote efficiency in healthcare.
Management Support (Aldosari, 2004; Dansky et al., UHC 5 Electronic health technology integration will
1999; Morton, 2008). help build a sustainable and resilient healthcare system.